Privacy-Preserving Language Models

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CAMP: Cumulative Agentic Masking and Pruning for Privacy Protection in Multi-Turn LLM Conversations
Apr 16, 2026Aman PanjwaniPrivacy-Preserving Language Models Privacy Leakage in Language Models
Independent Researcher
Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation
Apr 8, 2026Qian Ma, Sarah RajtmajerPrivacy-Preserving Language Models Synthetic Data Generation
Information Sciences and Technology, The Pennsylvania State University
CARE: Privacy-Compliant Agentic Reasoning with Evidence Discordance
Apr 1, 2026Haochen Liu, Weien Li, Rui Song +6Privacy-Preserving Language Models Healthcare
University of Cambridge · McGill University · MBZUAI - Mohamed bin Zayed University of Artificial Intelligence +2
MAPLE: Metadata Augmented Private Language Evolution
Feb 26, 2026Eli Chien, Yuzheng Hu, Ryan McKenna +3Privacy-Preserving Language Models Synthetic Data Generation
National Taiwan University · University of Illinois Urbana-Champaign · Google Research +1
AlienLM: Alienization of Language for API-Boundary Privacy in Black-Box LLMs
Jan 30, 2026Jaehee Kim, Pilsung KangPrivacy-Preserving Language Models Privacy Leakage in Language Models
Department of Industrial Engineering, Seoul National University, South Korea.
VoxPrivacy: A Benchmark for Evaluating Interactional Privacy of Speech Language Models
Jan 27, 2026Yuxiang Wang, Hongyu Liu, Dekun Chen +2Privacy-Preserving Language Models Language Model Safety Evaluation
The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute · Amphion Technology Co., Ltd. +1
Improved Bounds for Private and Robust Alignment
Dec 29, 2025Wenqian Weng, Yi He, Xingyu ZhouPrivacy-Preserving Language Models Corruption Robustness
Department of Electrical and Computer Engineering, Wayne State University, Detroit, USA.
User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios
Oct 23, 2025Xiaoyuan Wu, Roshni Kaushik, Wenkai Li +2LLM Evaluation LLM-as-a-Judge
Fujitsu Research of America, Inc. · Carnegie Mellon University
Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training
Jul 2, 2025Ismail Labiad, Mathurin Videau, Matthieu Kowalski +4Privacy-Preserving Language Models Evolutionary Optimization
Meta FAIR · Université Paris-Saclay · LISN, Inria, CNRS +2
Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks
Apr 24, 2025Yang Liu, Kejia Zhang, Bingjie Yan +11Privacy-Preserving Language Models LLM Fine-Tuning
The Hong Kong Polytechnic University, China · Institute for AI Industry Research, Tsinghua University, China · Institute for AI Industry Research, Tsinghua University, China and Shanghai Jiao Tong University, China +3
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
Oct 20, 2024Shang Wang, Tianqing Zhu, Dayong Ye +1Privacy-Preserving Language Models LLM Unlearning
School of Computer Science, University of Technology Sydney · Faculty of Data Science, City University of Macau
BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines
Date pendingBo Ma, Jinsong Wu, Weiqi YanData Leakage Privacy-Preserving Language Models
Auckland University of Technology Auckland, New Zealand · Guilin University of Electronic Technology Guilin, China
Demystifying the Privacy-Utility Trade-off in LLM Interactions
Date pendingZhenhua Liu, Zhanxu Xie, Junjie Yu +3Privacy-Preserving Language Models Privacy Leakage in Language Models
Soochow University · Beihang University · Suzhou City University +2

Apr 16, 2026, cs.CRJ/K move · Enter open · S save

CAMP: Cumulative Agentic Masking and Pruning for Privacy Protection in Multi-Turn LLM Conversations

Aman Panjwani

Independent Researcher

Privacy-Preserving Language Models Privacy Leakage in Language Models

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The deployment of Large Language Models in agentic, multi-turn conversational settings has introduced a class of privacy vulnerabilities that existing protection mechanisms are not designed to address. Current approaches to Personally Identifiable Information (PII) masking operate on a per-turn basis, scanning each user message in isolation and replacing detected entities with typed placeholders before forwarding sanitized text to the model. While effective against direct identifier leakage within a single message, these methods are fundamentally stateless and fail to account for the compounding privacy risk that emerges when PII fragments accumulate across conversation turns. A user who separately discloses their name, employer, location, and medical condition across several messages has revealed a fully re-identifiable profile - yet no individual message would trigger a per-turn masker. We formalize this phenomenon as Cumulative PII Exposure (CPE) and propose CAMP (Cumulative Agentic Masking and Pruning), a cross-turn privacy protection framework for multi-turn LLM conversations. CAMP maintains a session-level PII registry, constructs a co-occurrence graph to model combination risk between entity types, computes a CPE score after each turn, and triggers retroactive masking of conversation history when the score crosses a configurable threshold. We evaluate CAMP on four synthetic multi-turn scenarios spanning healthcare, hiring, finance, and general conversation, demonstrating that per-turn baselines expose re-identifiable profiles that CAMP successfully neutralizes while preserving full conversational utility.

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